Analysis Code and Results: Granger-Predictive Influence Pipeline Across Five Pharmacological fMRI Datasets (LSD, Psilocybin, DMT, Ketamine, Propofol)

Abstract

Analysis code (Python) and processed results (CSV files) supporting the manuscript: Malone, R.W. (2026). "A Unified Granger-Predictive Influence Pipeline Reveals Drug-Specific Thalamic Dynamic Signatures Across Five Pharmacological Resting-State fMRI Datasets." NeuroImage: Reports (submitted). Contents include: normalized cascade fraction and strength metrics, dynamic functional connectivity variance summaries, layer-specific temporal stability rankings, net directional influence scores, bootstrap confidence intervals, and edge-level statistics across LSD, psilocybin, DMT, ketamine, and propofol conditions. Primary analysis script: normalized_cascade_and_driver_test.py.

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